Get a list of all possible tool names.
langchain.agents.initialize_agent(tools: Sequence[langchain.tools.base.BaseTool], llm: langchain.llms.base.BaseLLM, agent: Optional[str] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, agent_path: Optional[str] = None, agent_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Load an agent executor given tools and LLM.
Parameters
tools – List of tools this agent has access to.
llm – Language model to use as the agent.
agent – 
A string that specified the agent type to use. Valid options are:zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
chat-zero-shot-react-description,
chat-conversational-react-description,
If None and agent_path is also None, will default tozero-shot-react-description.
callback_manager – CallbackManager to use. Global callback manager is used if
not provided. Defaults to None.
agent_path – Path to serialized agent to use.
agent_kwargs – Additional key word arguments to pass to the underlying agent
**kwargs – Additional key word arguments passed to the agent executor
Returns
An agent executor
langchain.agents.load_agent(path: Union[str, pathlib.Path], **kwargs: Any) → langchain.agents.agent.Agent[source]#
Unified method for loading a agent from LangChainHub or local fs.
langchain.agents.load_tools(tool_names: List[str], llm: Optional[langchain.llms.base.BaseLLM] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → List[langchain.tools.base.BaseTool][source]#
Load tools based on their name.
Parameters